H-Index & Metrics Top Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Computer Science H-index 60 Citations 11,942 377 World Ranking 1521 National Ranking 145

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Mechanical engineering
  • Mathematical optimization

Liang Gao focuses on Mathematical optimization, Job shop scheduling, Flow shop scheduling, Algorithm and Metaheuristic. His study in Mathematical optimization is interdisciplinary in nature, drawing from both Scheduling, Dynamic priority scheduling and Benchmark. His study looks at the relationship between Job shop scheduling and fields such as Genetic algorithm, as well as how they intersect with chemical problems.

His Flow shop scheduling study which covers Hybrid algorithm that intersects with Automated planning and scheduling. His work deals with themes such as Failure probability, Design of experiments, Kriging, Upper and lower bounds and Robustness, which intersect with Algorithm. In his work, Machining is strongly intertwined with Optimization problem, which is a subfield of Metaheuristic.

His most cited work include:

  • A New Convolutional Neural Network-Based Data-Driven Fault Diagnosis Method (431 citations)
  • An effective hybrid particle swarm optimization algorithm for multi-objective flexible job-shop scheduling problem (349 citations)
  • A New Deep Transfer Learning Based on Sparse Auto-Encoder for Fault Diagnosis (274 citations)

What are the main themes of his work throughout his whole career to date?

Liang Gao mostly deals with Mathematical optimization, Algorithm, Job shop scheduling, Artificial intelligence and Scheduling. His Mathematical optimization study integrates concerns from other disciplines, such as Scheduling and Flow shop scheduling. His Flow shop scheduling study results in a more complete grasp of Dynamic priority scheduling.

As part of the same scientific family, Liang Gao usually focuses on Algorithm, concentrating on Benchmark and intersecting with Differential evolution. His Job shop scheduling research is multidisciplinary, incorporating elements of Energy consumption, Efficient energy use and Metaheuristic. His studies in Artificial intelligence integrate themes in fields like Fault, Machine learning and Pattern recognition.

He most often published in these fields:

  • Mathematical optimization (40.80%)
  • Algorithm (14.84%)
  • Job shop scheduling (14.42%)

What were the highlights of his more recent work (between 2019-2021)?

  • Mathematical optimization (40.80%)
  • Artificial intelligence (11.26%)
  • Job shop scheduling (14.42%)

In recent papers he was focusing on the following fields of study:

His scientific interests lie mostly in Mathematical optimization, Artificial intelligence, Job shop scheduling, Topology optimization and Algorithm. Liang Gao studied Mathematical optimization and Kriging that intersect with Active learning. His Artificial intelligence research includes themes of Fault, Machine learning and Pattern recognition.

His Job shop scheduling research incorporates themes from Energy consumption, Scheduling, Metaheuristic and Heuristic. Liang Gao has researched Topology optimization in several fields, including Topology, Interpolation, Topology and Homogenization. Liang Gao interconnects Optimization problem and Benchmark in the investigation of issues within Evolutionary algorithm.

Between 2019 and 2021, his most popular works were:

  • A transfer convolutional neural network for fault diagnosis based on ResNet-50 (50 citations)
  • A system active learning Kriging method for system reliability-based design optimization with a multiple response model (44 citations)
  • Real-time estimation error-guided active learning Kriging method for time-dependent reliability analysis (38 citations)

In his most recent research, the most cited papers focused on:

  • Artificial intelligence
  • Mechanical engineering
  • Mathematical optimization

Mathematical optimization, Topology optimization, Algorithm, Job shop scheduling and Artificial intelligence are his primary areas of study. His research on Mathematical optimization frequently connects to adjacent areas such as Interval. His Algorithm research includes elements of Swarm behaviour, Mode, Surrogate model and Confidence interval.

His Job shop scheduling study incorporates themes from Linear programming and Energy consumption. His Artificial intelligence research incorporates elements of Machine learning and Pattern recognition. The various areas that Liang Gao examines in his Flow shop scheduling study include Scheduling and Efficient energy use.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Top Publications

A New Convolutional Neural Network-Based Data-Driven Fault Diagnosis Method

Long Wen;Xinyu Li;Liang Gao;Yuyan Zhang.
IEEE Transactions on Industrial Electronics (2018)

623 Citations

An effective hybrid particle swarm optimization algorithm for multi-objective flexible job-shop scheduling problem

Guohui Zhang;Xinyu Shao;Peigen Li;Liang Gao.
Computers & Industrial Engineering (2009)

537 Citations

An effective genetic algorithm for the flexible job-shop scheduling problem

Guohui Zhang;Liang Gao;Yang Shi.
Expert Systems With Applications (2011)

365 Citations

A New Deep Transfer Learning Based on Sparse Auto-Encoder for Fault Diagnosis

Long Wen;Liang Gao;Xinyu Li.
IEEE Transactions on Systems, Man, and Cybernetics (2019)

339 Citations

An effective hybrid genetic algorithm and tabu search for flexible job shop scheduling problem

Xinyu Li;Liang Gao.
International Journal of Production Economics (2016)

253 Citations

Integration of process planning and scheduling-A modified genetic algorithm-based approach

Xinyu Shao;Xinyu Li;Liang Gao;Chaoyong Zhang.
Computers & Operations Research (2009)

241 Citations

An improved fruit fly optimization algorithm for continuous function optimization problems

Quan-Ke Pan;Quan-Ke Pan;Hong-Yan Sang;Jun-Hua Duan;Liang Gao.
Knowledge Based Systems (2014)

224 Citations

Cellular particle swarm optimization

Yang Shi;Hongcheng Liu;Liang Gao;Guohui Zhang.
Information Sciences (2011)

216 Citations

EXACT ONE-PERIODIC AND TWO-PERIODIC WAVE SOLUTIONS TO HIROTA BILINEAR EQUATIONS IN (2+1) DIMENSIONS

Wen-Xiu Ma;Wen-Xiu Ma;Ruguang Zhou;Liang Gao;Liang Gao.
Modern Physics Letters A (2009)

194 Citations

A multi-objective genetic algorithm based on immune and entropy principle for flexible job-shop scheduling problem

Xiaojuan Wang;Liang Gao;Chaoyong Zhang;Xinyu Shao.
The International Journal of Advanced Manufacturing Technology (2010)

183 Citations

Profile was last updated on December 6th, 2021.
Research.com Ranking is based on data retrieved from the Microsoft Academic Graph (MAG).
The ranking h-index is inferred from publications deemed to belong to the considered discipline.

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